Location Privacy Preservation Research Articles

Fully privacy-preserving location recommendation in outsourced environments

Currently, location-based services (LBS) have been widely used in real-world settings, including restaurant and travel recommendations. To reduce workload and improve query efficiency, a service provider usually outsources its services to a powerful cloud server. However, the service provider’s database and users’ queries always contain sensitive information, so their leakage to the cloud may raise serious privacy concerns. Although some existing schemes have been proposed to address the privacy problems, they are impractical in real-world LBS due to some issues in privacy, accuracy, or heavy computation costs for query users. In order to overcome these problems, we propose a fully privacy-preserving location recommendation scheme that supports multi-attribute queries and returns accurate results based on the recommendation condition. Specifically, based on the Paillier cryptosystem, we first propose a secure equal test (SET) protocol to check whether two encrypted values are equal. Second, with our proposed protocols, we develop a privacy-preserving location recommendation scheme without revealing anything about the service provider or query users. Finally, we analyze the security of our scheme in the semi-honest model and show that the privacy of the service provider and query users is well protected. Meanwhile, we evaluate the performance of our scheme using synthetic datasets. The experimental results demonstrate that our proposed scheme is practical in real-world applications.

Framework of Geofence Service using Dummy Location Privacy Preservation in Vehicular Cloud Network

With the increasing prevalence of different mobile apps, many applications require users to enable the location service on their devices. For example, the geofence service can be defined as establishing virtual geographical boundaries. Enabling this service triggers entering and exiting the boundary area and notifies the users and trusted third parties. The foremost concern of using geofence is the privacy of location coordinates shared among different applications. In this paper, a framework called ‘TIET-GEO’ is proposed that allows users to define the geofence boundary; in addition, it monitors Global Positioning System (GPS) devices in real-time when they enter/exit a specific area. The proposed framework also proposes a dummy privacy preservation algorithm to generate K-dummy locations around the real trajectories when the user requests the Point Of Interest (POI) from the Location-Based Services (LBS). This article aims to enhance the location privacy preservation in geofence service, by generating a k-dummy location around the user location based on the radius size of the geofence area. The proposed framework uses token keys authentication to authorize the users in the Vehicular Cloud Network (VCN) service by generating secret token keys authentication between the client and services. The results obtained show the effectiveness of the proposed framework was on parameters like flexibility and reliability of responses from different sources, such as smart IoT devices and datasets.

A Novel Pseudonym Changing Scheme for Location Privacy Preservation in Sparse Traffic Areas

A Novel Pseudonym Changing Scheme for Location Privacy Preservation in Sparse Traffic Areas

Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks

Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection.

RESAT: A Utility-Aware Incentive Mechanism-Based Distributed Spatial Cloaking

Distributed spatial cloaking (DSC) enables users to enjoy precise location-based service (LBS) with location privacy preserving. An incentive mechanism is necessary to encourage users to cooperate. However, due to the inappropriate design of incentive mechanisms, the existing works cause low user benefits and fail to encourage users, ruining the expected incentive effect. Moreover, introducing a third party to manage users’ information also causes the existing works to disclose users’ privacy and be unpractical. To address these issues, we propose a utility-aware incentive mechanism-based distributed spatial cloaking (RESAT). By the idea of utility theory and optimization theory, RESAT devises basic and extended incentive mechanisms. The two mechanisms for assuming that all users are honest and that malicious users provide unreasonable locations. RESAT proposes an incentive mechanism-based cloaking cooperation without a third party, incorporating the developed mechanisms based on the blind signature. Theoretical analysis indicates that RESAT achieves incentive compatibility and is secure. Extensive experiments on the real data set show that compared with the existing works, RESAT enables 1 time more users to cooperate at best while eliminating the malicious behaviors that provide unreasonable locations. The required DSC construction time delay is limited.

Personalized Location Privacy With Road Network-Indistinguishability

The proliferation of location-based services (LBS) leads to increasing concern about location privacy. Location obfuscation is a promising privacy-preserving technique but yet to be adequately tailored for vehicles in road networks. Existing obfuscation schemes are based primarily on the Euclidean distances and can lead to infeasible results, e.g., off-road locations. In this paper, we define Road Network-Indistinguishability (RN-I) to evaluate obfuscation-based location privacy-preserving schemes in road networks. To protect drivers’ location privacy in road networks, we propose a Personalized Location Privacy-Preserving (PLPP) scheme and prove it achieves RN-I. The PLPP scheme employs a dual-obfuscation algorithm, consisting of a connection perturbation and an interval perturbation, to obfuscate on-road locations. An efficient personalization algorithm is designed for the PLPP scheme to fine-tune location privacy budgets for capturing drivers’ sensitive locations and privacy requirements. Experiments upon two real-world datasets confirm the location privacy-preserving capability, data utility, and efficiency of the proposed PLPP scheme.

Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation

Wireless sensor network (WSN) combines and transmit the data in internet of things (IoT) applications. The nodes in pervasive Wireless sensor network are battery-operated and it needed to develop an energy-efficient approach, which lessens the energy consumption and increases the network lifetime. The existing methods not provide effective cluster head (CH) selection and node computation.In this manuscript, Multi-Objective Cluster Head Based Energy Aware Routing using Self-attention based progressive generative adversarial network optimized with African vulture optimization is proposed for Secured Data aggregation in WSN (MOCH-SAPGAN-AVO). Initially, the proposed model performs the routing process through Cluster heads (CH). So, the data aggregation using Self-attention based progressive generative adversarial network selects CH based on the multi-objective fitness function. These multi-objective fitness functions consider factors, like energy, delay, throughput, distance between the nodes and cluster density. After the cluster head selection, the optimal path is used to transfer the data to the base station. The optimal path is based on three parameters, such as degree of satisfication, connectivity and Rate of service (RoS). Hence, these three parameters are optimized with the help of African vulture optimization approach for optimal path selection. Finally, the optimal path is used for transferring the aggregated data to the base station (BS) and vice versa. The proposed method is implemented in network simulator (NS2) tool and the performance of proposed method is estimated. The proposed method attains 30.39%, 34.64%, 45.36%, and 33.32% lower delay, 39.056%, 44.12% and 28.97% improved packet delivery ratio and 45.96%, 48.06%, and 38.16% lower packet drop compared with existing methods, like hybrid and energy-efficient secure data aggregation algorithm and fuzzy logic based secure data aggregation in wireless sensor network (FL-CHESDA-WSN), multi objective fostered male lion optimization algorithm (DA-WSN-MLOA) and an Adaptive Source Location Privacy Preservation Technique and Principle Component Analysis (IDAF-ASLPP-RR).

Source location privacy preservation in IoT-enabled event-driven WSNs

PurposePrivacy preservation is a significant concern in Internet of Things (IoT)-enabled event-driven wireless sensor networks (WSNs). Low energy utilization in the event-driven system is essential if events do not happen. When events occur, IoT-enabled sensor network is required to deal with enormous traffic from the concentration of demand data delivery. This paper aims to explore an effective framework for safeguarding privacy at source in event-driven WSNs.Design/methodology/approachThis paper discusses three algorithms in IoT-enabled event-driven WSNs: source location privacy for event detection (SLP_ED), chessboard alteration pattern (SLP_ED_CBA) and grid-based source location privacy (GB_SLP). Performance evaluation is done using simulation results and security analysis of the proposed scheme.FindingsThe sensors observe bound events or sensitive items within the network area in the field of interest. The open wireless channel lets an opponent search traffic designs, trace back and reach the start node or the event-detecting node. SLP_ED and SLP_ED_CBA provide better safety level results than dynamic shortest path scheme and energy-efficient source location privacy protection schemes. This paper discusses security analysis for the GB_SLP. Comparative analysis shows that the proposed scheme is more efficient on safety level than existing techniques.Originality/valueThe authors develop the privacy protection scheme in IoT-enabled event-driven WSNs. There are two categories of occurrences: nominal events and critical events. The choice of the route from source to sink relies on the two types of events: nominal or critical; the privacy level required for an event; and the energy consumption needed for the event. In addition, phantom node selection scheme is designed for source location privacy.

Tactile Routing for Location Privacy Preservation in Wireless Sensor Networks: A Game Theoretic Approach.

Location Privacy Preservation (LPP) in Wireless Sensor Networks (WSNs) during the era of the Internet of things and smart systems is a critical element in the success of WSNs. LPP in WSN can be stated as: given a WSN with an adversary aiming to unravel the location of critical nodes of a WSN, the goal of the WSN manager is to enshroud the location of the critical nodes via routing and/or encryption mechanisms. Typical research in the LPP of WSN routing involves developing and/or estimating the performance of a fixed routing protocol under a given attack mechanism. Motivated by advancements in network softwarization, in this work, we propose an approach where the WSN manager as well as the WSN adversary can deploy multiple routing and attack mechanisms, respectively. Initially, the proposed approach is formulated as a repeated two-player zero-sum game. The formulation is further extended to handle multiple objectives and incomplete information in the game matrix. In this work, the multiple objectives are handled via the epsilon constraint method. The presence of incomplete information in the formulation is modeled as interval based uncertainty. To sum, the proposed formulation ultimately boils down to linear programming problems, which can be efficiently solved. Numerical case studies to showcase the applicability of the proposed approach are illustrated in this work. Finally, discussion on obtaining the required data from any given WSN, discussion and interpretation of the formulation’s results, and future research direction of the current work is presented.

Location Privacy-Preserving Query Scheme Based on the Moore Curve and Multi-User Cache

With the rapid development of the Internet of Things, location-based services have emerged in many social and business fields. In obtaining the service, the user needs to transmit the query data to an untrusted location service provider for query and then obtain the required content. Most existing schemes tend to protect the user’s location privacy information while ignoring the user’s query privacy. This paper proposes a secure and effective query privacy protection scheme. The multi-user cache is used to store historical query results, reduce the number of communications between users and untrusted servers, and introduce trust computing for malicious users in neighbor caches, thereby reducing the possibility of privacy leakage. When the cache cannot meet the demand, the user’s location coordinates are converted using the Moore curve, processed using encryption technology, and sent to the location service provider to prevent malicious entities from accessing the transformed data. Finally, we simulate and evaluate the scheme on real datasets, and the experimental results demonstrate the safety and effectiveness of the scheme.

Guest Editorial: Special Section on Distributed Intelligence Over Internet of Things

N OWADAYS, billions of devices are connected to the In-ternet, enabling Internet of Things (IoT) systems widely deployed, such as smart city, smart healthcare and intelligent plant, to capture a great quantity of sensing data. Consequently, the data transmission, processing and analysis in IoT applications bring a great pressure to the central server. Fortunately, distributed intelligence becomes one of the potential solutions. Distributed intelligence can greatly relieve server pressures via plenty of terminal devices, and these devices collaboratively perceive and handle the mass data to improve the reliability, s-calability and security of industrial IoT systems. As future IoT system will embrace more wireless sensors and devices, the high-performance computing, high-bandwidth and low-latency communication are excessively required, many new research opportunities and challenges for distributed intelligence over Internet of things have arisen. To promote the development of distributed intelligence technology, this special section (SS) focuses on various technologies and platforms regarding industrial IoT systems. This special section received nearly 50 submitted manuscripts, out of which 10 of them have been accepted after a rigorous peer review. Each manuscript is reviewed by multiple rounds of review with at least three or four reviewers, the problems to be solved and the innovation of each manuscript are mainly concerned. Then the accepted papers are summarized as follows in details. Considering the joint optimization of the offloading decision and resource allocation under limited resource constraints in collaborative edge computing networks with multiple IIoT devices and MEC servers, an improved differential evolution algorithm [7] is proposed to minimize the weighted sum of cost of energy consumption and time delay, which can effectively reduce the system delay and energy consumption. In order to improve the performance of task scheduling in cloud computing, Attiya et al. [1] propose a novel hybrid swarm intelligence method MRFOSSA, which uses a modified Manta-Ray Foraging Optimizer (MRFO) and the Salp Swarm Algorithm (SSA). MRFOSSA is superior to other methods in terms of makespan time and cloud throughput. The research goal of the paper [5] is to design an intelligent computing offloading strategy for industrial applications in order to optimize costs and mitigate energy losses. Then the paper proposes to combine a fog controller and AI-based learning techniques so that the fog controller can intelligently assign tasks to the most appropriate fog devices and find the appropriate path to the target. Considering the resource utilization efficiency under dynamic overload requests and network states in IIoT, Chen et al. [2] propose DRL-based intelligent SFC orchestration scheme and jointly optimize the VNF deployment and SFC embedment by the improved DDQN algorithm, which can improve the performance of resource utilization rate, execution cost and delay compared with other representative schemes. To solve the problem of resource allocation and energy cost in Internet of Vehicles, Kong et al. [8] design a joint computing and caching framework and formulate the problem as a reinforcement learning problem to minimize the energy cost. On this basis, the optimization algorithm based on DDPG is proposed, which can effectively decrease energy costs. To reduce the query numbers of the object model when constructing adversarial examples, Zhang et al. [10] propose generating adversarial examples with shadow model (GASM), i.e., transfering the query operations to the designed shadow model, which can achieve high attack success rates. Chen et al. [3] revise a Decentralized-Wireless-Federated-Learning algorithm (DWFL) which utilizes the superposition property of the analog scheme. It can solve the problem of single failure, limited bandwidth resource and privacy protection in wireless federated learning algorithm, which can be applied widely in wireless IoT networks. To reduce the resource consumption in CNN-based applications, Jia et al. [6] propose the CNN-based Resource Optimization APProach which utilizes model compression and computation sharing to optimize inner-model and inter-model respectively, and the comparison results show the superior performance in scalability and the decrease of resource cost. In mobile crowdsensing activities, Gao et al. [4] propose a differential Location Privacy-preserving Mechanism based on Trajectory obfuscation (LPMT) to protect the location privacy of mobile users, which includes three operations: stay points extraction, stay points obfuscation and stay points sampling. In order to mimic the task-free bottom-up visual attention process by predicting salient regions on natural images, Umer et al. [9] propose a Pseudo Knowledge Distillation (PKD) model based on knowledge distillation and pseudo labelling technique, which is computationally efficient and suitable for real-time on-device saliency prediction.

Quantifying Location Privacy for Navigation Services in Sustainable Vehicular Networks

Current connected and autonomous vehicles will contribute to various and green vehicular services. However, sharing personal data with untrustworthy Navigation Service Providers (NSPs) raises serious location concerns. To address this issue, many Location Privacy-Preserving Mechanisms (LPPMs) have been proposed. In addition, several quantification methods have been designed to help understand location privacy and illustrate how location privacy is leaked. However, their assessment is insufficient due to the incomplete assumptions about the adversary’s model. In particular, users tend to request the same navigation routes from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">home</i> to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workplace</i> and acquire traffic information along the route. An adversary can collect the coordinates of adjacent locations and infer the two true locations. In this paper, we provide a formal framework for the analysis of LPPMs in navigation services. Our framework captures extra information that is available to an adversary performing localization attacks. By formalizing the adversary’s performance, we also propose and justify two new metrics to quantify location privacy in navigation services, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accuracy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility</i> . We assess the efficacy of two popular LPPMs for location privacy, i.e., differential privacy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -anonymity. Experimental results demonstrate that the adversary can recover users’ locations with a high probability.

CrowdHB: A Decentralized Location Privacy-Preserving Crowdsensing System Based on a Hybrid Blockchain Network

With the advent of the Internet of Things (IoT), crowdsensing, as a new emerging application of the IoT that employs ubiquitous mobile users with smartphones for data collection and processing, has further deepened our knowledge. However, the problems of the current crowdsensing systems regarding system security, user privacy, and user payment (UP) raise serious privacy and security concerns, which affect participants’ adoption of the system. The Blockchain technology allows for nondeterministic multiple parties to interact with each other anonymously in a network that is not fully trusted. In this article, we propose a new decentralized crowdsensing system, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdHB</i> . Unlike other blockchain-based crowdsensing systems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdHB</i> adopts a hybrid blockchain architecture and uses smart contracts to achieve location privacy preservation and ensure data quality while improving the system performance. Furthermore, to optimize task assignments to mobile users, we propose a location privacy-preserving optimization mechanism (LPPOM) and the approach of consistency optimization (ACO) to achieve a tradeoff between user privacy and system performance. The extensive experimental results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdHB</i> outperforms the other crowdsensing systems in terms of task success rate and performance for a large number of mobile users and tasks.

LA-MQTT : Location-aware Publish-subscribe Communications for the Internet of Things

Nowadays, several Internet of Things (IoT) deployments use publish-subscribe paradigms to disseminate IoT data to a pool of interested consumers. At the moment, the most widespread standard for such scenarios is MQTT. We also register an increasing interest in IoT-enabled Location-Based Services, where data must be disseminated over a target area and its spatial relevance and the current positions of the consumers must be taken into account. Unfortunately, the MQTT protocol does not support location awareness, and hence it may result in notifying consumers that are geographically far from the data source, causing increased network overhead and poor Quality of Service (QoS). We address the issue by proposing LA-MQTT , an extension to standard MQTT supporting spatial-aware publish-subscribe communications on IoT scenarios. LA-MQTT is broker-agnostic and fully backward compatible with standard MQTT. As monitoring the position of subscribers over time may cause privacy concerns, LA-MQTT carefully supports location privacy preservation, for which the optimal tradeoff with the QoS of the spatial notifications is addressed via a learning-based algorithm. We demonstrate the effectiveness of LA-MQTT by experimentally evaluating its features via large-scale hybrid simulations, including real and virtual components. Finally, we provide a Proof of Concept real implementation of an LA-MQTT scenario.

A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation

In the process of crowdsensing, tasks allocation is an important part of the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may be aware of the location of each other, and then threaten their privacy. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the process of task matching by applicants and publishers is also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publishers can be preserved. Finally, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. In the last part of this paper, four types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of our proposed scheme.

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles.

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.

PCP: A Pseudonym Change Scheme for Location Privacy Preserving in VANETs

In vehicular ad hoc networks (VANETs), pseudonym change is considered as the vital mechanism to support vehicles’ anonymity. Due to the complicated road conditions and network environment, it is a challenge to design an efficient and adaptive pseudonym change protocol. In this paper, a pseudonym change protocol for location privacy preserving (PCP) is proposed. We first present the requirements of pseudonym change in different scenarios. According to variable network states and road conditions, vehicles are able to take different pseudonym change strategies to resist the tracking by global passive adversaries. Furthermore, the registration protocol, authentication protocol, pseudonym issuance protocol, and pseudonym revocation protocol are introduced for the pseudonym management mechanism. As a consequence, it is not feasible for global passive adversaries to track a vehicle for a long time and obtain the trajectory of the vehicle. The analysis results show that the security and performance of PCP are improved compared with the traditional ones.

A Blockchain-Based Location Privacy-Preserving Scheme in Location-Based Service

With the rapid popularization and development of location-based services (LBS), location privacy preserving has become a mainstream concern. Existing anonymity-based methods rely mainly on anonymous location selection based on geographic information, which neglects the semantic information on the location, and behavior of self-interested users may raise a privacy vulnerability. A blockchain-based approach for location privacy protection schemes has been proposed to address these issues. In this scheme, the privacy level of the location is improved using private chain-based collaborative anonymous communication with anonymous set construction methods that consider the semantic diversity associated with the related user. Compared with other existing methods, evaluation based on real-world datasets shows that the proposed method prevents location privacy leakage from collaborative users and constructs a semantical anonymity set, thus effectively protecting the user’s location privacy.

On the privacy protection of indoor location dataset using anonymization

Indoor positioning is becoming more popular with increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS). Location fingerprinting is widely deployed in indoor localization, and extensive daily use of different kinds of mobile devices in indoor environments results in generating a huge amount of location data of people. However, publishing this location based dataset by Location Service Provider (LSP) to any third party, such as researchers or commercial organizations, potentially threatens users’ privacy. Several studies have been done to anonymize the user location data, but protecting the users’ information against various attacks in an indoor environment is still a challenging issue. This paper proposes a novel framework for publishing privacy preserving location data employing five anonymization techniques: k-anonymity, ℓ-diversity, t-closeness, (α,k)-anonymity, and δ-presence. In the proposed framework, although the LSP cannot find the exact location of the users, it can provide them online location services at the same time and publish the anonymized privacy protected dataset afterward. The practical feasibility of applying the proposed framework is verified on both simulated and two real-world datasets. The results indicate that the published location dataset can protect the identity and location information of users while providing location information for third parties for further purposes.

A source location privacy preservation scheme in WSN-assisted IoT network by randomized ring and confounding transmission

A source location privacy preservation scheme in WSN-assisted IoT network by randomized ring and confounding transmission